半监督医学图像分割中伪标签的协同引导区域监督
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
AI总结：
针对半监督医学图像分割中伪标签的噪声污染问题，提出SGRS-Net框架，基于mean teacher网络结合Mix Augmentation模块和Region Loss Evaluation模块对伪标签进行区域划分与损失评估，在LA数据集上取得优于SOTA的性能。
摘要：
半监督学习因能利用大量未标记数据来增强模型鲁棒性而备受关注。伪标签是半监督学习中常用的策略。然而，现有方法常受噪声污染影响，从而损害模型性能。为应对这一挑战，我们提出了一种新颖的伪标签协同引导区域监督（SGRS-Net）框架。基于 mean teacher 网络，我们采用 Mix Augmentation 模块来增强未标记数据。通过评估增强前后的协同作用，我们将伪标签策略性地划分为不同区域。此外，我们引入了 Region Loss Evaluation 模块来评估每个划分区域的损失。在 LA 数据集上进行的大量实验表明，其性能优于最先进的技术，突显了我们框架的有效性与实用性。
英文摘要：
Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
